Drugs of abuse can provoke long-lasting behavioral change, in part by abnormally engaging neural plasticity mechanisms that underlie normal reinforcement-driven learning. In particular, critical features of drug addiction may arise from altered function of dorsal striatal circuits that handle the progressive automatization of behavioral responses. Striatal circuits are widely thought to operate using essentially similar principles as artificial reinforcement learning (RL) algorithms for adaptive decision-making. However, how specific components of these circuits map onto specific computational/behavioral functions remains controversial. Within dorsal striatum there are remarkable subregions called patches (or striosomes) that have very different connectivity and neurochemistry to the surrounding matrix. It has long been hypothesized that patches have a special role in reinforcement learning, helping to control evaluative feedback about the consequences of actions. However, testing such ideas has not been possible, largely due to technical limitations in distinguishing patch and matrix neurons in behaving animals. We are now in a position to overcome this critical barrier to progress, and greatly advance understanding of just how rewarding experiences lead to altered behavior. The goals of this application are a) to complete development of a new generation of electrophysiological probes for high-density recording from identified striatal locations, and b) to use these devices to compare the activity patterns of patch and matrix neurons during reinforcement learning tasks. In this way we will test the specific hypothesis that patch neurons encode signals related to reward prediction. An accurate description of the roles of striatal compartments in reinforcement learning would greatly assist investigations into how abused drugs hijack normal decision-making. In addition, the new devices would be of broad application for investigating neural coding in both striatum and other neural circuits.